docstr: ivy.Array instance method variant of ivy.cov. This method simply
  wraps the function, and so the docstring for ivy.cov also applies to
  this method with minimal changes.
  
  Parameters
  ----------
  self
      a 1D or 2D input array, with a numeric data type.
  x2
      optional second 1D or 2D input array, with a numeric data type.
      Must have the same shape as ``self``.
  rowVar
      optional variable where each row of input is interpreted as a variable
      (default = True). If set to False, each column is instead interpreted as a
      variable.
  bias
      optional variable for normalizing input (default = False) by (N - 1) where
      N is the number of given observations. If set to True, then normalization
      is instead by N. Can be overridden by keyword ``ddof``.
  ddof
      optional variable to override ``bias`` (default = None). ddof=1 will return
      the unbiased estimate, even with fweights and aweights given. ddof=0 will
      return the simple average.
  fweights
      optional 1D array of integer frequency weights; the number of times each
      observation vector should be repeated.
  aweights
      optional 1D array of observation vector weights. These relative weights are
      typically large for observations considered "important" and smaller for
      observations considered less "important". If ddof=0 is specified, the array
      of weights can be used to assign probabilities to observation vectors.
  dtype
      optional variable to set data-type of the result. By default, data-type
      will have at least ``float64`` precision.
  out
      optional output array, for writing the result to. It must have a shape that
      the inputs broadcast to.
  
  Returns
  -------
  ret
      an array containing the covariance matrix of an input matrix, or the
      covariance matrix of two variables. The returned array must have a
      floating-point data type determined by Type Promotion Rules and must be
      a square matrix of shape (N, N), where N is the number of variables in the
      input(s).
  
  Examples
  --------
  >>> x = ivy.array([[1, 2, 3],
  ...                [4, 5, 6]])
  >>> y = x[0].cov(x[1])
  >>> print(y)
  ivy.array([[1., 1.],
         [1., 1.]])
  
  >>> x = ivy.array([1,2,3])
  >>> y = ivy.array([4,5,6])
  >>> z = x.cov(y)
  >>> print(z)
  ivy.array([[1., 1.],
         [1., 1.]])
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code: def cov(
      self: ivy.Array,
      x2: Optional[Union[ivy.Array, ivy.NativeArray]] = None,
      /,
      *,
      rowVar: bool = True,
      bias: bool = False,
      ddof: Optional[int] = None,
      fweights: Optional[ivy.Array] = None,
      aweights: Optional[ivy.Array] = None,
      dtype: Optional[type] = None,
  ) -> ivy.Array:
      """ivy.Array instance method variant of ivy.cov. This method simply
      wraps the function, and so the docstring for ivy.cov also applies to
      this method with minimal changes.
  
      Parameters
      ----------
      self
          a 1D or 2D input array, with a numeric data type.
      x2
          optional second 1D or 2D input array, with a numeric data type.
          Must have the same shape as ``self``.
      rowVar
          optional variable where each row of input is interpreted as a variable
          (default = True). If set to False, each column is instead interpreted as a
          variable.
      bias
          optional variable for normalizing input (default = False) by (N - 1) where
          N is the number of given observations. If set to True, then normalization
          is instead by N. Can be overridden by keyword ``ddof``.
      ddof
          optional variable to override ``bias`` (default = None). ddof=1 will return
          the unbiased estimate, even with fweights and aweights given. ddof=0 will
          return the simple average.
      fweights
          optional 1D array of integer frequency weights; the number of times each
          observation vector should be repeated.
      aweights
          optional 1D array of observation vector weights. These relative weights are
          typically large for observations considered "important" and smaller for
          observations considered less "important". If ddof=0 is specified, the array
          of weights can be used to assign probabilities to observation vectors.
      dtype
          optional variable to set data-type of the result. By default, data-type
          will have at least ``float64`` precision.
      out
          optional output array, for writing the result to. It must have a shape that
          the inputs broadcast to.
  
      Returns
      -------
      ret
          an array containing the covariance matrix of an input matrix, or the
          covariance matrix of two variables. The returned array must have a
          floating-point data type determined by Type Promotion Rules and must be
          a square matrix of shape (N, N), where N is the number of variables in the
          input(s).
  
      Examples
      --------
      >>> x = ivy.array([[1, 2, 3],
      ...                [4, 5, 6]])
      >>> y = x[0].cov(x[1])
      >>> print(y)
      ivy.array([[1., 1.],
             [1., 1.]])
  
      >>> x = ivy.array([1,2,3])
      >>> y = ivy.array([4,5,6])
      >>> z = x.cov(y)
      >>> print(z)
      ivy.array([[1., 1.],
             [1., 1.]])
      """
      return ivy.cov(
          self._data,
          x2,
          rowVar=rowVar,
          bias=bias,
          ddof=ddof,
          fweights=fweights,
          aweights=aweights,
          dtype=dtype,
      )
  
file_path: ivy/ivy/data_classes/array/experimental/statistical.py
imports: import abc
  from typing import Optional, Union, Tuple, Sequence
  import ivy
label_desc: Use image node_id 11 for calling the _ArrayWithStatisticalExperimental obj's underlying member method code with example usage: obj.cov() and returns: ivy